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http://dx.doi.org/10.4196/kjpp.2019.23.5.311

Current status and future direction of digital health in Korea  

Shin, Soo-Yong (Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University)
Publication Information
The Korean Journal of Physiology and Pharmacology / v.23, no.5, 2019 , pp. 311-315 More about this Journal
Abstract
Recently, digital health has gained the attention of physicians, patients, and healthcare industries. Digital health, a broad umbrella term, can be defined as an emerging health area that uses brand new digital or medical technologies involving genomics, big data, wearables, mobile applications, and artificial intelligence. Digital health has been highlighted as a way of realizing precision medicine, and in addition is expected to become synonymous with health itself with the rapid digitization of all health-related data. In this article, we first define digital health by reviewing the diverse range of definitions among academia and government agencies. Based on these definitions, we then review the current status of digital health, mainly in Korea, suggest points that are missing from the discussion or ought to be added, and provide future directions of digital health in clinical practice by pointing out certain key points.
Keywords
Artificial intelligence; Digital health; eHealth; Government regulation; Mobile health;
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1 Shortliffe EH, Sepulveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320:2199-2200.   DOI
2 Goldhahn J, Rampton V, Spinas GA. Could artificial intelligence make doctors obsolete? BMJ. 2018;363:k4563.   DOI
3 Maddox TM, Rumsfeld JS, Payne PRO. Questions for artificial intelligence in health care. JAMA. 2019;321:31-32.   DOI
4 Israni ST, Verghese A. Humanizing artificial intelligence. JAMA. 2019;321:29-30.   DOI
5 Saria S, Butte A, Sheikh A. Better medicine through machine learning: what's real, and what's artificial? PLoS Med. 2018;15:e1002721.   DOI
6 Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019;28:231-237.   DOI
7 Medicine in the digital age. Nat Med. 2019;25:1.   DOI
8 Norgeot B, Glicksberg BS, Butte AJ. A call for deep-learning healthcare. Nat Med. 2019;25:14-15.   DOI
9 Gottesman O, Johansson F, Komorowski M, Faisal A, Sontag D, Doshi-Velez F, Celi LA. Guidelines for reinforcement learning in healthcare. Nat Med. 2019;25:16-18.   DOI
10 He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25:30-36.   DOI
11 Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44-56.   DOI
12 Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380:1347-1358.   DOI
13 Lee H, Shin SY, Seo M, Nam GB, Joo S. Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks. Sci Rep. 2016;6:32390.   DOI
14 Kourtis LC, Regele OB, Wright JM, Jones GB. Digital biomarkers for Alzheimer's disease: the mobile/wearable devices opportunity. NPJ Digit Med. 2019;2:9.   DOI
15 Kwon JM, Lee Y, Lee Y, Lee S, Park J. An algorithm based on deep learning for predicting in-hospital cardiac arrest. J Am Heart Assoc. 2018;7:e008678.   DOI
16 Park SH, Do KH, Choi JI, Sim JS, Yang DM, Eo H, Woo H, Lee JM, Jung SE, Oh JH. Principles for evaluating the clinical implementation of novel digital healthcare devices. J Korean Med Assoc. 2018;61:765-775.   DOI
17 Duggal R, Brindle I, Bagenal J. Digital healthcare: regulating the revolution. BMJ. 2018;360:k6.   DOI
18 Institute of Medicine. A continuously learning health care system. In: Smith M, Saunders R, Stuckhardt L, editors. Best care at lower cost: the path to continuously learning health care in America. Washington (DC): National Academies Press (US); 2013. p.136.
19 Park YR, Shin SY. Status and direction of healthcare data in Korea for artificial intelligence. Hanyang Med Rev. 2017;37:86-92.   DOI
20 Budrionis A, Bellika JG. The learning healthcare system: where are we now? a systematic review. J Biomed Inform. 2016;64:87-92.   DOI
21 Mathews SC, McShea MJ, Hanley CL, Ravitz A, Labrique AB, Cohen AB. Digital health: a path to validation. NPJ Digit Med. 2019;2:38.   DOI
22 Gunning D, Aha DW. DARPA's explainable artificial intelligence (XAI) program. AI Mag. 2019;40:44-58.   DOI
23 Adadi A, Berrada M. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access. 2018;6:52138-52160.   DOI
24 Towards trustable machine learning. Nat Biomed Eng. 2018;2:709-710.   DOI
25 Kim CY, Kang G, Lee JS, Kim BY, Kim YI, Shin Y. Introduction and the current status of hospital information systems. J Korean Soc Med Inform. 1999;5:27-35.   DOI
26 Shah NR. Health care in 2030: will artificial intelligence replace physicians? Ann Intern Med. 2019;170:407-408.   DOI
27 Mesko B, Drobni Z, Benyei E, Gergely B, Gyorffy Z. Digital health is a cultural transformation of traditional healthcare. Mhealth. 2017;3:38.   DOI
28 Steinhubl SR, Topol EJ. Digital medicine, on its way to being just plain medicine. NPJ Digit Med. 2018;1:20175.   DOI
29 U.S. Food & Drug Administration. Digital health innovation action plan. 2019. 8p.
30 Choi YS. Digital healthcare: the future of medicine. Seoul: Cloud9. Forthcoming 2019.
31 Payne PRO, Bernstam EV, Starren JB. Biomedical informatics meets data science: current state and future directions for interaction. JAMIA Open. 2018;1:136-141.   DOI